1. Field
Exemplary embodiments relate to methods and apparatuses for generating a magnetic resonance image.
2. Description of the Related Art
Magnetic resonance imaging (MRI) apparatuses are used to acquire an image of a subject by using a magnetic field, to accurately diagnose diseases since MRI apparatuses show stereoscopic image of bones, discs, joints, nerve ligaments, etc., at a desired angle.
An MRI apparatus acquires a magnetic resonance (MR) signal and reconstructs the acquired MR signal to output an image. The MRI apparatus acquires an MR signal by using radio frequency (RF) coils, a permanent magnet, a gradient coil, and the like. When an MR signal is acquired, an invalid signal may be generated in a joining parts of RF coils. For example, since the MR signal is not measured in a joining part of adjacent RF coils, the non-measured invalid signal may appear as noise in the reconstructed magnetic resonance image. In addition, while K-space data acquired in RF coils are reconstructed as a magnetic resonance image, noise existing in the K-space data may be amplified.
Accordingly, an acquired MR signal has to be corrected by performing image processing such as calibration or the like, to output a magnetic resonance image without the artifacts and noise.
MRI methods of processing an acquired MR signal include sensitivity encoding (SENSE) method, a generalized auto-calibrating partially parallel acquisition (GRAPPA) method, and the like.
An image-based imaging method, such as the SENSE method, calculates coil sensitivity information by separating an image corresponding to each individual coil via self-calibration in an image space. An image of each individual coil is acquired by performing inverse Fourier transformation on central portion data of a K-space, which has been Nyquist-sampled. However, when reconstructing a magnetic resonance image by using the coil sensitivity information, image-based self-calibration needs very accurate coil sensitivity information.
Accordingly, a large number of calibration signals is required in a central portion of K-space data, and therefore, a time taken to form an image increases. In addition, when a field of view (FOV) is smaller than a subject to be imaged, the image-based self-calibration may cause residual aliasing artifacts during image reconstruction.
A K-space-based imaging method, such as the GRAPPA method, calculates spatial correlations or convolution kernels between a calibration signal and an adjacent measured source signal by performing self-calibration. The GRAPPA method does not need accurate coil sensitivity information and is not limited in reconstruction of the FOV. However, when data of an image signal is damaged due to the noise and spatial correlations are changed, residual aliasing artifacts and amplified noise occur in a reconstructed image.
Accordingly, there is a need for methods and apparatuses which are capable of reducing aliasing artifacts and noise in the magnetic resonance image when data of an image signal is damaged.